期刊
NEUROCOMPUTING
卷 70, 期 16-18, 页码 3011-3018出版社
ELSEVIER
DOI: 10.1016/j.neucom.2006.07.016
关键词
GAP-RBF; FGAP-RBF; neuron significance; DEKF
This paper presents the performance evaluation of the recently developed Growing and Pruning Radial Basis Function (GAP-RBF) algorithm for classification problems. Earlier GAP-RBF was evaluated only for function approximation problems. Improvements to GAP-RBF for enhancing its performance in both accuracy and speed are also described and the resulting algorithm is referred to as Fast GAP-RBF (FGAP-RBF). Performance comparison of FGAP-RBF algorithm with GAP-RBF and the Minimal Resource Allocation Network (MRAN) algorithm based on four benchmark classification problems, viz. Phoneme, Segment, Satimage and DNA are presented. The results indicate that FGAP-RBF produces higher classification accuracy with reduced computational complexity. (c) 2006 Elsevier B.V. All rights reserved.
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